Indoor self-localization via bluetooth low energy beacons

Indoor localization is concerned with mapping sensory data to physical locations inside buildings. Location of a user or a mobile device is an essential part of the context, and is therefore very useful for pervasive computing applications. Many proposals exist for solving the localization problem, typically based on image or radio signal processing, though the problem is still generally considered to be open, especially when costs and privacy constraints play an important role. In this paper, we propose a solution based on the emerging Bluetooth Low Energy (BLE) standard and off-the-shelf hardware. Such approach proves to satisfy economic constraints, while challenging in terms of accurate location. To translate beacon signals into locations, we consider several approaches, i.e., cosine similarity, nearest neighbourhood classification, and the nearest beacon. Our experiments indicate a vector based approach as the most suited one. In fact, we show its effectiveness in an actual office deployment consisting of five indoor areas: three multiuser offices, a social corner, and a hallway. We achieve 90% and 80% for accuracy and F-measure, respectively

Low-power Appliance Recognition using Recurrent Neural Networks

Indoor energy consumption can be understood by breaking overall power consumption down into individual components and appliance activations. The clas- sification of components of energy usage is known as load disaggregation or ap- pliance recognition. Most of the previous efforts address the separation of devices with high energy demands. In many contexts though, such as an office, the devices to separate are numerous, heterogeneous, and have low consumptions. The disag- gregation problem becomes then more challenging and, at the same time, crucial for understanding the user context. In fact, from the disaggregation one can deduce the number of people in an office room, their activities, and current energy needs. In this paper, we review the characteristics of office appliances load disaggregation efforts. We then illustrate a proposal for a classification model based on Recur- rent Neural Network (RNN). RNN is used to infer device activation from aggre- gated energy consumptions. The approach shows promising results in recognizing 14 classes of 5 different devices being operated in our office, reaching 99.4% of Cohen’s Kappa measure

Cloud Ready Applications Composed via HTN Planning

Modern software applications are increasingly deployed and distributed on infrastructures in the Cloud, and then offered as a service. Before the deployment process happens, these applications are being manually - or with some predefined scripts - composed from various smaller interdependent components. With the increase in demand for, and complexity of applications, the composition process becomes an arduous task often associated with errors and a suboptimal use of computer resources. To alleviate such a process, we introduce an approach that uses planning to automatically and dynamically compose applications ready for Cloud deployment. The industry may benefit from using automated planning in terms of support for product variability, sophisticated search in large spaces, fault tolerance, near-optimal deployment plans, etc. Our approach is based on Hierarchical Task Network (HTN) planning as it supports rich domain knowledge, component modularity, hierarchical representation of causality, and speed of computation. We describe a deployment using a formal component model for the Cloud, and we propose a way to define and solve an HTN planning problem from the deployment one. We employ an existing HTN planner to experimentally evaluate the feasibility of our approach